Introduction and Rationale
The recent CITE editorial “Picturing the Theory of GenAI: Frameworks for Teaching and Learning in the Age of Artificial Intelligence” (Cherner et al., 2026) offered a timely synthesis of conceptual frameworks for guiding educators’ use of generative AI (genAI). The contributors emphasized the importance of theory-building as a way to move our field toward more intentional, pedagogically grounded use of genAI. This editorial is a response and proposes an extension to shift the perspective from AI literacy (Jin et al., 2024; Laupichler et al., 2022) to AI design agency, and from educators as consumers of genAI tools and using existing AI tools in assessments or assignments with students (McDonald et al., 2025; Yan et al., 2023) to educators as creators of AI-driven learning experiences (AI-X). Emerging “vibe coding” and low-code tools now enable educators to design and share their own AI-mediated applications (Kobiella et al., 2026). This shift from consuming to creating AI-X raises new pedagogical, ethical, and societal challenges that have not been fully addressed by existing frameworks. This shift changes the stakes and introduces new layers of responsibility around ethics, pedagogy, and impact (Mak et al., 2025, 2026): What supports do creators need to design ethical, learner-centered AI-X? How can frameworks and tools operationalize those supports? If educators are to become responsible, ethical creators of AI-X, then frameworks must guide design decisions, scaffold evaluation, and surface ethical considerations in the context of creating AI-X.
The AI-X Framework: Codesign Process
We present a design-based implementation research project (DBIR; Fishman & Penuel, 2018) that launched in August 2025 (see Figure 1). Using DBIR, we position AI-X development as a socio-technical endeavor that requires diverse cultural, ethical, and pedagogical perspectives. Our approach aligns with key principles of DBIR by:
- Centering problems of practice from the perspectives of multiple stakeholders
- Committing to iterative, collaborative design (codesign)
- Systematically exploring implementation and learning in conjunction with developing theory and knowledge
- Building capacity for sustainable systems transformation
We adapted the formal design-based implementation research (DBIR) approach to cycles of design, creation, implementation, and revision to inform the development of the AI-X Framework. Our project roadmap (Figure 1; Mak et al., 2026) highlights the codesign process. During the design sessions, we invited faculty and staff from across our institution with technology expertise and/or AI-X creator experience, based on recommendations from enterprise leaders and early innovators/users of CreateAI Builder (https://ai.asu.edu/technical-foundation/createai-builder), to join the AI-X design team (n = 26). We met over 2 days with sessions designed to answer the following questions: What are the essential elements that constitute a high-quality AI-X? How can we support our community in adopting principled and ethical approaches to creating AI-X? Following the working sessions with the design team, we met as a research team to analyze and synthesize the data from the design team into a draft AI-X Framework. We then presented the draft Framework to the community of practice composed of additional faculty (n = 30) representing education, social work, engineering, psychology, health sciences, and humanities departments across our institution for ongoing feedback and codesign sessions (January 2025 – April 2026).
Figure 1
Project Roadmap
Table 1 outlines DBIR principles and the ways in which we enact them in this study. Through this multi-phase, institution-wide codesign process, the AI-X Framework’s relevance and applicability are strengthened. By engaging stakeholders across roles and disciplines in the codesign process, the AI-X Framework is iteratively shaped by the contexts in which it will be used, thereby ensuring that its ethical commitments are enacted in the Framework itself as well as throughout its development.
Table 1
DBIR Principles and Enactments
| DBIR Principle | Enactment of Principle |
|---|---|
| Centering problems of practice from the perspectives of multiple stakeholders | Gathering input from design team and focus group members |
| Committing to iterative, collaborative design | Participatory approach, intentionally diverse representation of faculty and staff in design team and focus groups, iterative feedback and design cycles allowing for reflection and revisions |
| Systematically exploring implementation and learning in conjunction with developing theory and knowledge | Development of the AI-X Framework is a research study, using diverse data sources to inform analysis and theory building. |
| Building capacity for sustainable systems transformation | Designing, deploying, and scaling the implementation and adoption of the AI-X Framework is critical for building capacity of a community of AI-X creators. This community ensures sustainable systems transformation by supporting high-quality, ethical AI-X with strong pedagogical value. A digital version of the AI-X Framework is currently in development to further support scalability and broader adoption. |
The AI-X Framework: Design Ecosystem
The resultant AI-X Framework, derived from our codesign process (Figure 2), is a design ecosystem of three tools that pose the following questions: Just because we can design AI-X, should we? And in designing AI-X, how can we do so in ways that center their pedagogical value? The AI-X Framework project operationalizes the values presented in the original editorial (Cherner et al., 2026) through a concrete set of tools (i.e., AI-X Compass, AI-X Guide, AI-X Toolkit) that support AI-X creators through the various stages of AI-X development.
The three tools (i.e., AI-X Compass, Guide, and Toolkit) constitute the AI-X design ecosystem, with each scaffolding a different stage of AI-X creation (Figure 3).
- AI-X Compass supports early ideation through a self-assessment that helps AI-X creators clarify the use case and learning purpose, examine alignment across four dimensions (learning value, user experience, responsibility, and feasibility), surface risks and unresolved questions, and receive an action-oriented recommendation that maps the idea to one of four decision quadrants (see Figure 4).
- AI-X Guide is an interactive, web-based, self-assessment instrument that guides AI-X development from initial prototyping of the AI-X through its rollout. The Guide is grounded in eight themes that specify what constitutes socially responsible, technically principled, and pedagogically grounded AI-X design. The eight themes (human-centeredness; access & impact; trust, transparency, & governance; pedagogical grounding; usability & adoption; context & purpose clarity; responsible personalization; technical sustainability) were derived through the codesign sessions with each theme containing definitions and criteria to guide both development and evaluation of AI-X (see Figure 5).
- AI-X Toolkit provides creators of AI-X with a suite of tools to operationalize design priorities with intentionality. Grounded on the Learning Engineering Process (Goodell & Kolodner, 2023), it provides support for ethical design of AI-X across four stages: defining the challenge, designing and developing the AI-X, implementing the AI-X to test its effectiveness, and investigating its performance (see Figure 6).
Figure 2
Screenshot of AI-X Framework Home Page (https://aix-framework.lei-tech.org/home)
Figure 3
Visual Mapping of AI-X Framework Design Ecosystem
Figure 4
Screenshot of the Sample AI-X Compass Evaluation Result (https://aix-framework.lei-tech.org/compass)
Figure 5
Screenshot of AI-X Guide (https://aix-framework.lei-tech.org/compass)
Figure 6
Screenshot of AI-X Toolkit (https://aix-framework.lei-tech.org/aix-toolkit-home)
Undergirding the AI-X Framework is Principled Innovation (PI; Gulesarian & Beghetto, 2025; Kristjánsson & VanderWeele, 2025), the ability to imagine new concepts, catalyze ideas, and form new solutions guided by principles that create conditions for human flourishing. Centering the questions of “We can innovate and create AI-X but should we, and how should we?” places ethical considerations at the core of AI-X creation. Within the AI-X Framework, we highlight reflective questions within and across the four interconnected clusters of character assets – moral, civic, intellectual, and performance. These questions are intended to surface critical questions related to being an ethical and principled creator of AI-X (see Figure 7).
Figure 7
Screenshot of Principled Innovation in the AI-X Framework (https://aix-framework.lei-tech.org/principled-innovation)
The AI-X Framework: Implications and Future Work
With the shift from consuming to creating AI-X, it is critical to center principled, iterative, participatory, and human-centered approaches to innovation. Together, Principled Innovation and Learning Engineering grounds the AI-X Framework with its suite of tools (AI-X Compass, AI-X Guide, and AI-X Toolkit). This Framework is significant both in its unique approach to 1) codesign at a large, diverse institution, and 2) supporting reflective and principled AI-X creators grounded in ethics, accessibility, and intentionality. Designing, deploying, and scaling the AI-X Framework and its capacity to support AI-X creators are critical for building capacity toward sustainable systems transformation by supporting high-quality, ethical AI-X with pedagogical value. While the AI-X Framework was developed at a higher education institution, it is intended for anyone who creates AI-X for teaching and learning. The guidance and content cut across K–16 and beyond.
Our future work will include field-testing the digital version of the AI-X Framework for a range of use cases. For example, faculty have used the AI-X Framework to guide teams’ development of tools at hackathon events, and some intend to integrate the Framework into their courses for students to critically evaluate existing AI-X tools. The AI-X Framework has also been integrated into professional learning experiences for faculty and staff in conjunction with opportunities for them to create and evaluate their own AI-X. Ongoing data collection, analysis, and synthesis will continue to inform future iterations and potential use cases of this Framework.
The editorial frameworks discussed by Cherner et al. (2026) illuminate key considerations to guide the adoption and instructional use of genAI in education, including whether it supports meaningful learning, how it reshapes teaching and learning practices, whose values and assumptions are embedded within its outputs, and the extent to which it enhances or constrains human agency. The AI-X Framework addresses these same concerns through a complementary focus on the design and evaluation of AI-driven learning experiences. The AI-X Compass aligns with the GenAI Use in Teaching and Learning Matrix by supporting educators in examining whether a proposed genAI use is pedagogically valuable, ethically sound, feasible to implement, and responsive to learner needs. The AI-X Guide complements the CATCH framework by providing actionable criteria for evaluating AI-enabled tools, content, and learning experiences through pedagogical, cultural, ethical, and critical perspectives. The AI-X Toolkit extends human-centered AI principles into design practice by curating questions, methods, and resources that support responsible AI-X development through the learning engineering process, from identifying a challenge to designing, implementing, and investigating an AI-driven learning experience. Collectively, these frameworks suggest that responsible genAI integration requires more than technical proficiency or access to emerging tools. It requires intentional structures that enable educators, designers, developers, and institutions to critically examine questions of value, equity, evidence, agency, and impact across the full lifecycle of AI-enabled learning.
Author Note
This research was supported by a grant from the Kern Family Foundation. Special thanks to our codesign partners on the design team and community of practice and colleagues at the Learning Engineering Institute, particularly our development team: Zeinab Serhan, Omkar Shelar, and Nishad Anil Patne.
References
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